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license: apache-2.0 |
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# CogView2 |
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## Model description |
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**CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers** |
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- [Paper](https://arxiv.org/abs/2204.14217) |
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- [GitHub Repo](https://github.com/THUDM/CogView2) |
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### Abstract |
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The development of the transformer-based text-to-image models are impeded by its slow generation and complexity for high-resolution images. In this work, we put forward a solution based on hierarchical transformers and local parallel auto-regressive generation. We pretrain a 6B-parameter transformer with a simple and flexible self-supervised task, Cross-modal general language model (CogLM), and finetune it for fast super-resolution. The new text-to-image system, CogView2, shows very competitive generation compared to concurrent state-of-the-art DALL-E-2, and naturally supports interactive text-guided editing on images. |
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## BibTeX entry and citation info |
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```bibtex |
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@article{ding2022cogview2, |
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title={CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers}, |
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author={Ding, Ming and Zheng, Wendi and Hong, Wenyi and Tang, Jie}, |
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journal={arXiv preprint arXiv:2204.14217}, |
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year={2022} |
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} |
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``` |
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